α-Gal antigen-deficient rabbits with GGTA1 gene disruption via CRISPR/Cas9
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Previous studies have identified the carbohydrate epitope Galα1-3Galβ1-4GlcNAc-R (termed the α-galactosyl epitope), known as the α-Gal antigen as the primary xenoantigen recognized by the human immune system. The α-Gal antigen is regulated by galactosyltransferase (GGTA1), and α-Gal antigen-deficient mice have been widely used in xenoimmunological studies, as well as for the immunogenic risk evaluation of animal-derived medical devices. The objective of this study was to develop α-Gal antigen-deficient rabbits by GGTA1 gene editing with the CRISPR/Cas9 system. RESULTS: The mutation efficiency of GGTA1 gene-editing in rabbits was as high as 92.3% in F0 pups. Phenotype analysis showed that the α-Gal antigen expression in the major organs of F0 rabbits was decreased by more than 99.96% compared with that in wild-type (WT) rabbits, and the specific anti-Gal IgG and IgM antibody levels in F1 rabbits increased with increasing age, peaking at approximately 5 or 6 months. Further study showed that GGTA1 gene expression in F2-edited rabbits was dramatically reduced compared to that in WT rabbits. CONCLUSIONS: α-Gal antigen-deficient rabbits were successfully generated by GGTA1 gene editing via the CRISPR/Cas9 system in this study. The feasibility of using these α-Gal antigen-deficient rabbits for the in situ implantation and residual immunogenic risk evaluation of animal tissue-derived medical devices was also preliminarily confirmed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it